Google Vertex AI Claude Code: An expert overview for 2025

It feels like every week there’s a new team-up between a big cloud company and a top AI lab, and the partnership between Google Cloud and Anthropic is a big one. They’ve brought Anthropic’s impressive Claude models, particularly the developer-focused Claude Code, onto Google’s powerful Vertex AI platform. It’s a duo that promises a whole lot of power for anyone building custom AI solutions.

But what does it really take to get something like this up and running? This guide will walk you through what Google Vertex AI Claude Code is, what the setup looks like, its most common uses, and the often-tricky pricing model. We’ll cover what you need to know to figure out if it’s the right tool for your team.

What is Google Vertex AI Claude Code?

First thing to know: this isn’t a single, off-the-shelf product. It’s a combination of three different pieces working together: Google’s AI platform, Anthropic’s AI models, and a specialized tool for coding.

Google Vertex AI: The enterprise AI platform

Think of Vertex AI as Google Cloud’s mission control for all things AI. It’s a platform that handles all the backend infrastructure for building, deploying, and scaling machine learning models. This lets your team focus on building cool stuff instead of managing servers. It comes loaded with tools like the Model Garden, which has over 200 different AI models to choose from, and an Agent Builder for creating your own custom AI agents.

Anthropic’s Claude: The AI model family

Anthropic’s Claude models are a family of large language models known for being pretty good at reasoning, handling a ton of information at once, and having strong safety features built-in. The family has a few different options, each suited for different jobs:

• Opus: The most powerful model, built for really complex, multi-step problems.

• Sonnet: The balanced option, offering a great mix of smarts and speed.

• Haiku: The fastest and leanest model, perfect for when you need a nearly instant response.

Vertex AI gives you access to the latest versions, including Claude Sonnet 4.5 and Opus 4.1, so you can use some of the newest AI tech directly within the Google Cloud world.

Claude Code: The developer’s collaborator

Claude Code is Anthropic’s special tool designed to help with the entire software development process. It does more than just write code snippets; it can help plan out big projects, debug tough problems, and keep codebases clean over time. You can use it through a terminal interface or a VS Code extension, making it a pretty convenient sidekick for developers.

A screenshot of the Claude Code assistant integrated within the Visual Studio Code IDE, demonstrating how Google Vertex AI Claude Code assists developers.

Setting up Google Vertex AI Claude Code: What’s involved?

Getting Claude Code running on Vertex AI is an amazing option for technical teams, but let’s be clear: it’s not a simple plug-and-play setup. The whole process is very developer-focused and requires a good handle on cloud environments.

To even begin, you’ll need a Google Cloud Platform account with billing enabled and the "gcloud" command-line tool installed. Then you have to enable the Vertex AI API in your project. After that, you find the Claude models in the Vertex AI Model Garden and request access, which can sometimes take a day or two to get approved.

Once you’re in, the real work starts. As the official setup documentation shows, you’ll need to set several environment variables to point Claude Code to Vertex AI, like "CLAUDE_CODE_USE_VERTEX" and "ANTHROPIC_VERTEX_PROJECT_ID".

these variables often need to be set globally for everything to work, which can be a small but frustrating hiccup.

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You’ll also need to sort out the right IAM permissions, like "roles/aiplatform.user", so your local machine can talk to your cloud project.

This kind of technical, multi-step process is great for engineering teams building custom apps from the ground up. But for business teams in support or IT who just need a solution that works out of the box, this can be a total non-starter. This is exactly why tools like eesel AI exist. eesel is designed to be completely self-serve, letting anyone connect their help desk and knowledge sources with one-click integrations and get going in minutes, not days.

Key capabilities and business use cases

For companies that have the engineering muscle to get through the setup, combining Claude and Vertex AI opens the door to some seriously impressive applications.

Advanced agentic coding and cybersecurity

Developers can use Claude Sonnet 4.5 on Vertex AI to manage complex, long-term coding jobs. We’re not just talking about writing a single function here. This is about planning and executing entire software projects or even having the AI autonomously find and patch security holes before they become a problem.

An image displaying the security guardrail feature in Google Vertex AI Claude Code, highlighting its cybersecurity capabilities.

Building complex AI agents and workflows

This setup is perfect for building custom AI agents that can handle tasks with multiple steps, pull from different data sources, and manage various tools. Google’s Agent Development Kit (ADK) provides a framework to help build these sophisticated agents and bring complicated workflows to life.

This video explains how to build powerful AI agents using Claude within Google Cloud's Vertex AI platform.

Of course, building a custom agent from scratch takes a lot of development time and effort. For businesses that need AI agents for specific roles like customer support or ITSM, eesel AI’s agent offers a fully customizable workflow engine ready to go. You can shape an AI’s persona, limit its knowledge to certain documents, and create custom actions, like looking up order info in Shopify or sorting tickets in Zendesk, all without touching a line of code.

Research and financial analysis

The reasoning and data-crunching skills of the Claude models are also great for things like research and financial analysis. You can build apps that sift through tons of data, spot trends, and spit out detailed reports, helping your teams make better decisions.

Understanding the costs: Pricing

Alright, let’s talk about money. Pricing is a huge deal for any business, and with a pay-as-you-go model like Vertex AI, things can get complicated fast. The final bill isn’t just about the AI model you use; it’s a mix of model usage, computing power, and other platform services.

First, let’s look at the cost of the Anthropic models. You’re typically charged per 1,000 characters (or tokens) for both the input (what you ask the AI) and the output (what it gives you back).

Source: Vertex AI Pricing Page

But that’s just one piece of the puzzle. The total cost of running your app on Vertex AI also includes charges for the computing resources it uses. That means you’re paying for vCPU hours, RAM, and maybe even GPU hours for training and running your models. Trying to forecast these costs can be a real headache, especially as your usage grows. Things like prompt caching can help lower costs on repeated questions, but that just adds another layer of complexity to manage.

This variable pricing can lead to some surprisingly high monthly bills, making it tough for business departments to budget effectively. It’s a big reason why many companies prefer platforms like eesel AI. Our pricing is straightforward and predictable, based on a fixed number of AI interactions each month. We never charge per resolution, so you won’t get a nasty surprise on your bill after a busy support month. Your costs stay the same, even when things get hectic.

Is Google Vertex AI Claude Code the right fit for your team?

So, who is this powerful tech combo really for? The ideal user is an engineering team with serious cloud experience that needs total control to build highly customized AI apps and agents from the ground up.

For most other teams, the main hurdles are pretty obvious:

• It’s very technical: You need dedicated developers just for the setup, configuration, and ongoing upkeep.

• The costs are unpredictable: The usage-based pricing makes it incredibly hard to budget and keep spending under control.

• It takes a long time to see results: Building a production-ready agent or workflow from scratch can easily take months of development.

The simpler, smarter alternative for business teams

For business teams that need results now, not next quarter, eesel AI offers a much more direct route. It’s built to solve the exact problems that foundational platforms like Vertex AI create for non-technical users.

With eesel AI, you can go live in minutes, not months. Our platform is genuinely self-serve, with one-click integrations for the tools you’re already using, like Zendesk, Slack, and Confluence. You can get started and see real value right away, without having to schedule a sales call or pull in your dev team.

You can also unify your knowledge instantly. eesel AI automatically learns from your past support tickets and connects to all your existing knowledge bases, so it gives accurate, helpful answers from the very first day.

Finally, you can test with confidence. Our simulation mode lets you test your AI setup on thousands of your own historical tickets, giving you an accurate forecast of its resolution rate before you ever turn it on for customers. It’s a risk-free way to make sure everything works perfectly.

Final thoughts on Google Vertex AI Claude Code

There’s no question that the Google Vertex AI and Claude Code combo offers incredible power and flexibility for developers. It makes it possible to create custom, top-of-the-line AI solutions that can handle some of the most complex challenges out there.

However, for business teams in customer service, IT, and internal support who just need to automate tasks and get things done now, a specialized platform is usually the faster and more cost-effective choice. The right tool really depends on your team’s goals, resources, and timeline. If you’re after raw power and have the engineering team to manage it, Vertex AI is a fantastic option. If you need speed, simplicity, and predictable costs, a solution built for business teams might be a much better fit.

Ready to see how quickly you can get a powerful AI agent working for your team? Set up your first eesel AI agent in minutes.